{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Base imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.optim import Optimizer\n",
    "\n",
    "\n",
    "import collections\n",
    "import h5py, sys\n",
    "import gzip\n",
    "import os\n",
    "import math\n",
    "\n",
    "\n",
    "try:\n",
    "    import cPickle as pickle\n",
    "except:\n",
    "    import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkdir(paths):\n",
    "    if not isinstance(paths, (list, tuple)):\n",
    "        paths = [paths]\n",
    "    for path in paths:\n",
    "        if not os.path.isdir(path):\n",
    "            os.makedirs(path)\n",
    "\n",
    "from __future__ import print_function\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n",
    "def humansize(nbytes):\n",
    "    i = 0\n",
    "    while nbytes >= 1024 and i < len(suffixes)-1:\n",
    "        nbytes /= 1024.\n",
    "        i += 1\n",
    "    f = ('%.2f' % nbytes)\n",
    "    return '%s%s' % (f, suffixes[i])\n",
    "\n",
    "\n",
    "def get_num_batches(nb_samples, batch_size, roundup=True):\n",
    "    if roundup:\n",
    "        return ((nb_samples + (-nb_samples % batch_size)) / batch_size)  # roundup division\n",
    "    else:\n",
    "        return nb_samples / batch_size\n",
    "\n",
    "def generate_ind_batch(nb_samples, batch_size, random=True, roundup=True):\n",
    "    if random:\n",
    "        ind = np.random.permutation(nb_samples)\n",
    "    else:\n",
    "        ind = range(int(nb_samples))\n",
    "    for i in range(int(get_num_batches(nb_samples, batch_size, roundup))):\n",
    "        yield ind[i * batch_size: (i + 1) * batch_size]\n",
    "\n",
    "def to_variable(var=(), cuda=True, volatile=False):\n",
    "    out = []\n",
    "    for v in var:\n",
    "        if isinstance(v, np.ndarray):\n",
    "            v = torch.from_numpy(v).type(torch.FloatTensor)\n",
    "\n",
    "        if not v.is_cuda and cuda:\n",
    "            v = v.cuda()\n",
    "\n",
    "        if not isinstance(v, Variable):\n",
    "            v = Variable(v, volatile=volatile)\n",
    "\n",
    "        out.append(v)\n",
    "    return out\n",
    "  \n",
    "def cprint(color, text, **kwargs):\n",
    "    if color[0] == '*':\n",
    "        pre_code = '1;'\n",
    "        color = color[1:]\n",
    "    else:\n",
    "        pre_code = ''\n",
    "    code = {\n",
    "        'a': '30',\n",
    "        'r': '31',\n",
    "        'g': '32',\n",
    "        'y': '33',\n",
    "        'b': '34',\n",
    "        'p': '35',\n",
    "        'c': '36',\n",
    "        'w': '37'\n",
    "    }\n",
    "    print(\"\\x1b[%s%sm%s\\x1b[0m\" % (pre_code, code[color], text), **kwargs)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "def shuffle_in_unison_scary(a, b):\n",
    "    rng_state = np.random.get_state()\n",
    "    np.random.shuffle(a)\n",
    "    np.random.set_state(rng_state)\n",
    "    np.random.shuffle(b)\n",
    "    \n",
    "    \n",
    "import torch.utils.data as data\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Datafeed(data.Dataset):\n",
    "\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)\n",
    "\n",
    "class DatafeedImage(data.Dataset):\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        img = Image.fromarray(np.uint8(img))\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "class BaseNet(object):\n",
    "    def __init__(self):\n",
    "        cprint('c', '\\nNet:')\n",
    "\n",
    "    def get_nb_parameters(self):\n",
    "        return np.sum(p.numel() for p in self.model.parameters())\n",
    "\n",
    "    def set_mode_train(self, train=True):\n",
    "        if train:\n",
    "            self.model.train()\n",
    "        else:\n",
    "            self.model.eval()\n",
    "\n",
    "    def update_lr(self, epoch, gamma=0.99):\n",
    "        self.epoch += 1\n",
    "        if self.schedule is not None:\n",
    "            if len(self.schedule) == 0 or epoch in self.schedule:\n",
    "                self.lr *= gamma\n",
    "                print('learning rate: %f  (%d)\\n' % self.lr, epoch)\n",
    "                for param_group in self.optimizer.param_groups:\n",
    "                    param_group['lr'] = self.lr\n",
    "\n",
    "    def save(self, filename):\n",
    "        cprint('c', 'Writting %s\\n' % filename)\n",
    "        torch.save({\n",
    "            'epoch': self.epoch,\n",
    "            'lr': self.lr,\n",
    "            'model': self.model,\n",
    "            'optimizer': self.optimizer}, filename)\n",
    "\n",
    "    def load(self, filename):\n",
    "        cprint('c', 'Reading %s\\n' % filename)\n",
    "        state_dict = torch.load(filename)\n",
    "        self.epoch = state_dict['epoch']\n",
    "        self.lr = state_dict['lr']\n",
    "        self.model = state_dict['model']\n",
    "        self.optimizer = state_dict['optimizer']\n",
    "        print('  restoring epoch: %d, lr: %f' % (self.epoch, self.lr))\n",
    "        return self.epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear_2L(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(Linear_2L, self).__init__()\n",
    "        \n",
    "        n_hid = 1200\n",
    "        \n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        \n",
    "        self.fc1 = nn.Linear(input_dim, n_hid)\n",
    "        self.fc2 = nn.Linear(n_hid, n_hid)\n",
    "        self.fc3 = nn.Linear(n_hid, output_dim)\n",
    "        \n",
    "        # choose your non linearity\n",
    "        #self.act = nn.Tanh()\n",
    "        #self.act = nn.Sigmoid()\n",
    "        self.act = nn.ReLU(inplace=True)\n",
    "        #self.act = nn.ELU(inplace=True)\n",
    "        #self.act = nn.SELU(inplace=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        x = x.view(-1, self.input_dim) # view(batch_size, input_dim)\n",
    "        # -----------------\n",
    "        x = self.fc1(x)\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        x = self.fc2(x)\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        y = self.fc3(x)\n",
    "\n",
    "        return y\n",
    "    \n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_groups = list(model.parameters())\n",
    "\n",
    "# for param in param_groups:\n",
    "#     print(param.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom SGLD optimiser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.optim.optimizer import Optimizer, required\n",
    "class SGLD(Optimizer):\n",
    "    \"\"\"\n",
    "    SGLD optimiser based on pytorch's SGD. \n",
    "    Note that the weight decay is specified in terms of the gaussian prior sigma\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, params, lr=required, norm_sigma=0, addnoise=True):\n",
    "        \n",
    "        weight_decay = 1/(norm_sigma**2)\n",
    "        \n",
    "        if weight_decay < 0.0:\n",
    "            raise ValueError(\"Invalid weight_decay value: {}\".format(weight_decay))\n",
    "        if lr is not required and lr < 0.0:\n",
    "            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n",
    "        \n",
    "        defaults = dict(lr=lr, weight_decay=weight_decay, addnoise=addnoise)\n",
    "        \n",
    "        super(SGLD, self).__init__(params, defaults)\n",
    "\n",
    "    def step(self):\n",
    "        \"\"\"\n",
    "        Performs a single optimization step.\n",
    "        \"\"\"\n",
    "        loss = None\n",
    "        \n",
    "        for group in self.param_groups:\n",
    "\n",
    "            weight_decay = group['weight_decay']\n",
    "            \n",
    "            for p in group['params']:\n",
    "                if p.grad is None:\n",
    "                    continue\n",
    "                d_p = p.grad.data\n",
    "                if weight_decay != 0:\n",
    "                    d_p.add_(weight_decay, p.data)\n",
    "                    \n",
    "                if group['addnoise']:\n",
    "                    \n",
    "                    langevin_noise = p.data.new(p.data.size()).normal_(mean=0, std=1)/np.sqrt(group['lr'])\n",
    "                    p.data.add_(-group['lr'],\n",
    "                                0.5*d_p + langevin_noise)\n",
    "                else:\n",
    "                    p.data.add_(-group['lr'], 0.5*d_p)\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pSGLD optimiser\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class pSGLD(Optimizer):\n",
    "    \"\"\"\n",
    "    RMSprop preconditioned SGLD using pytorch rmsprop implementation.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, params, lr=required, norm_sigma=0, alpha=0.99, eps=1e-8, centered=False, addnoise=True):\n",
    "        \n",
    "        weight_decay = 1/(norm_sigma**2)\n",
    "        \n",
    "        if weight_decay < 0.0:\n",
    "            raise ValueError(\"Invalid weight_decay value: {}\".format(weight_decay))\n",
    "        if lr is not required and lr < 0.0:\n",
    "            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n",
    "        defaults = dict(lr=lr, weight_decay=weight_decay, alpha=alpha, eps=eps, centered=centered, addnoise=addnoise)\n",
    "        super(pSGLD, self).__init__(params, defaults)\n",
    "        \n",
    "    def __setstate__(self, state):\n",
    "        super(pSGLD, self).__setstate__(state)\n",
    "        for group in self.param_groups:\n",
    "            group.setdefault('centered', False)\n",
    "\n",
    "    def step(self):\n",
    "        \"\"\"\n",
    "        Performs a single optimization step.\n",
    "        \"\"\"\n",
    "        loss = None\n",
    "\n",
    "        for group in self.param_groups:\n",
    "            \n",
    "            weight_decay = group['weight_decay']\n",
    "            for p in group['params']:\n",
    "                if p.grad is None:\n",
    "                    continue\n",
    "                d_p = p.grad.data\n",
    "                \n",
    "                state = self.state[p]\n",
    "                \n",
    "                if len(state) == 0:\n",
    "                    state['step'] = 0\n",
    "                    state['square_avg'] = torch.zeros_like(p.data)\n",
    "                    if group['centered']:\n",
    "                        state['grad_avg'] = torch.zeros_like(p.data)\n",
    "                        \n",
    "                square_avg = state['square_avg']\n",
    "                alpha = group['alpha']\n",
    "                state['step'] += 1\n",
    "                \n",
    "                if weight_decay != 0:\n",
    "                    d_p.add_(weight_decay, p.data)\n",
    "                \n",
    "                # sqavg x alpha + (1-alph) sqavg *(elemwise) sqavg\n",
    "                square_avg.mul_(alpha).addcmul_(1-alpha, d_p, d_p)\n",
    "                \n",
    "                if group['centered']:\n",
    "                    grad_avg = state['grad_avg']\n",
    "                    grad_avg.mul_(alpha).add_(1-alpha, d_p)\n",
    "                    avg = square_avg.cmul(-1, grad_avg, grad_avg).sqrt().add_(group['eps'])\n",
    "                else:\n",
    "                    avg = square_avg.sqrt().add_(group['eps'])\n",
    "                    \n",
    "#                 print(avg.shape)\n",
    "                if group['addnoise']:\n",
    "                    langevin_noise = p.data.new(p.data.size()).normal_(mean=0, std=1)/np.sqrt(group['lr'])\n",
    "                    p.data.add_(-group['lr'],\n",
    "                                0.5*d_p.div_(avg) + langevin_noise/torch.sqrt(avg))\n",
    "                    \n",
    "                else:\n",
    "                    p.data.addcdiv_(-group['lr'], 0.5*d_p, avg)\n",
    "\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import copy\n",
    "\n",
    "class Net_langevin(BaseNet):\n",
    "    eps = 1e-6\n",
    "\n",
    "    def __init__(self, lr=1e-3, channels_in=3, side_in=28, cuda=True, classes=10, N_train=60000, prior_sig=0):\n",
    "        super(Net_langevin, self).__init__()\n",
    "        cprint('y', ' Creating Net!! ')\n",
    "        self.lr = lr\n",
    "        self.schedule = None  # [] #[50,200,400,600]\n",
    "        self.cuda = cuda\n",
    "        self.channels_in = channels_in\n",
    "        self.prior_sig = prior_sig\n",
    "        self.classes = classes\n",
    "        self.N_train = N_train\n",
    "        self.side_in=side_in\n",
    "        self.create_net()\n",
    "        self.create_opt()\n",
    "        self.epoch = 0\n",
    "        \n",
    "        self.weight_set_samples = []\n",
    "\n",
    "        self.test=False\n",
    "\n",
    "    def create_net(self):\n",
    "        torch.manual_seed(42)\n",
    "        if self.cuda:\n",
    "            torch.cuda.manual_seed(42)\n",
    "\n",
    "        self.model = Linear_2L(input_dim=self.channels_in*self.side_in*self.side_in, output_dim=self.classes)\n",
    "        if self.cuda:\n",
    "            self.model.cuda()\n",
    "#             cudnn.benchmark = True\n",
    "\n",
    "        print('    Total params: %.2fM' % (self.get_nb_parameters() / 1000000.0))\n",
    "\n",
    "    def create_opt(self):\n",
    "#         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08,\n",
    "#                                           weight_decay=0)\n",
    "        self.optimizer = SGLD(params=self.model.parameters(), lr=self.lr, norm_sigma=self.prior_sig, addnoise=True)\n",
    "#         self.optimizer = pSGLD(params=self.model.parameters(), lr=self.lr, norm_sigma=self.prior_sig, addnoise=True)\n",
    "\n",
    "    #         self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.9)\n",
    "#         self.sched = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=10, last_epoch=-1)\n",
    "\n",
    "    def fit(self, x, y):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        out = self.model(x)\n",
    "        loss = F.cross_entropy(out, y, reduction='mean') # We use mean because we treat as an estimation of whole dataset\n",
    "        loss = loss * self.N_train \n",
    "            \n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        # out: (batch_size, out_channels, out_caps_dims)\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data*x.shape[0]/self.N_train, err\n",
    "\n",
    "    def eval(self, x, y, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out = self.model(x)\n",
    "\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "\n",
    "        probs = F.softmax(out, dim=1).data.cpu()\n",
    "\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def save_sampled_net(self, max_samples):\n",
    "        \n",
    "        if len(self.weight_set_samples) >= max_samples:\n",
    "            self.weight_set_samples.pop(0)\n",
    "            \n",
    "        self.weight_set_samples.append(copy.deepcopy(self.model.state_dict()))\n",
    "        \n",
    "        cprint('c', ' saving weight samples %d/%d' % (len(self.weight_set_samples), max_samples) )\n",
    "        \n",
    "        return None\n",
    "        \n",
    "    def sample_eval(self, x, y, Nsamples=0, logits=True, train=False):\n",
    "        if Nsamples == 0:\n",
    "            Nsamples = len(self.weight_set_samples)\n",
    "            \n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "        \n",
    "        out = x.data.new(Nsamples, x.shape[0], self.classes)\n",
    "        \n",
    "        # iterate over all saved weight configuration samples\n",
    "        for idx, weight_dict in enumerate(self.weight_set_samples):\n",
    "            if idx == Nsamples:\n",
    "                break\n",
    "            self.model.load_state_dict(weight_dict)\n",
    "            out[idx] = self.model(x)\n",
    "        \n",
    "        \n",
    "        if logits:\n",
    "            mean_out = out.mean(dim=0, keepdim=False)\n",
    "            loss = F.cross_entropy(mean_out, y, reduction='sum')\n",
    "            probs = F.softmax(mean_out, dim=1).data.cpu()\n",
    "            \n",
    "        else:\n",
    "            mean_out =  F.softmax(out, dim=2).mean(dim=0, keepdim=False)\n",
    "            probs = mean_out.data.cpu()\n",
    "            \n",
    "            log_mean_probs_out = torch.log(mean_out)\n",
    "            loss = F.nll_loss(log_mean_probs_out, y, reduction='sum')\n",
    "\n",
    "        pred = mean_out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def all_sample_eval(self, x, y, Nsamples):\n",
    "        if Nsamples == 0:\n",
    "            Nsamples = len(self.weight_set_samples)\n",
    "            \n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "        \n",
    "        out = x.data.new(Nsamples, x.shape[0], self.classes)\n",
    "        \n",
    "        # iterate over all saved weight configuration samples\n",
    "        for idx, weight_dict in enumerate(self.weight_set_samples):\n",
    "            if idx == Nsamples:\n",
    "                break\n",
    "            self.model.load_state_dict(weight_dict)\n",
    "            out[idx] = self.model(x)\n",
    "        \n",
    "        prob_out =  F.softmax(out, dim=2)\n",
    "        prob_out = prob_out.data\n",
    "\n",
    "        return prob_out\n",
    "    \n",
    "    def get_weight_samples(self, Nsamples=0):\n",
    "        weight_vec = []\n",
    "        \n",
    "        if Nsamples == 0 or Nsamples > len(self.weight_set_samples):\n",
    "            Nsamples = len(self.weight_set_samples)\n",
    "            \n",
    "        for idx, state_dict in enumerate(self.weight_set_samples):\n",
    "            if idx == Nsamples:\n",
    "                break\n",
    "                \n",
    "            for key in state_dict.keys():\n",
    "                if 'weight' in key:\n",
    "                    weight_mtx = state_dict[key].cpu().data\n",
    "                    for weight in weight_mtx.view(-1):\n",
    "                        weight_vec.append(weight)\n",
    "            \n",
    "        return np.array(weight_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 2.40M\n",
      "\u001b[36m\n",
      "Train:\u001b[0m\n",
      "  init cost variables:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 0/200, Jtr_pred = 0.332518, err = 0.100850, \u001b[31m   time: 5.272974 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.449600, err = 0.131500\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 1/200, Jtr_pred = 0.204831, err = 0.058117, \u001b[31m   time: 5.460800 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.193607, err = 0.053300\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 2/200, Jtr_pred = 0.184302, err = 0.051183, \u001b[31m   time: 5.305944 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.199471, err = 0.055800\n",
      "\u001b[0m\n",
      "it 3/200, Jtr_pred = 0.186226, err = 0.049983, \u001b[31m   time: 5.363621 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.171735, err = 0.043000\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 4/200, Jtr_pred = 0.163857, err = 0.044533, \u001b[31m   time: 5.477743 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.168283, err = 0.045000\n",
      "\u001b[0m\n",
      "it 5/200, Jtr_pred = 0.165283, err = 0.044067, \u001b[31m   time: 5.299246 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.152336, err = 0.043300\n",
      "\u001b[0m\n",
      "it 6/200, Jtr_pred = 0.154490, err = 0.042150, \u001b[31m   time: 5.412918 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.183699, err = 0.046700\n",
      "\u001b[0m\n",
      "it 7/200, Jtr_pred = 0.161372, err = 0.043633, \u001b[31m   time: 5.307577 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.214868, err = 0.057000\n",
      "\u001b[0m\n",
      "it 8/200, Jtr_pred = 0.159944, err = 0.043567, \u001b[31m   time: 5.304547 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.179113, err = 0.046400\n",
      "\u001b[0m\n",
      "it 9/200, Jtr_pred = 0.156224, err = 0.041867, \u001b[31m   time: 5.327288 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.178758, err = 0.048800\n",
      "\u001b[0m\n",
      "it 10/200, Jtr_pred = 0.156073, err = 0.042217, \u001b[31m   time: 5.229092 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.158346, err = 0.040500\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 11/200, Jtr_pred = 0.153146, err = 0.041417, \u001b[31m   time: 5.296193 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.185732, err = 0.048500\n",
      "\u001b[0m\n",
      "it 12/200, Jtr_pred = 0.149818, err = 0.043000, \u001b[31m   time: 5.201535 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.182823, err = 0.049900\n",
      "\u001b[0m\n",
      "it 13/200, Jtr_pred = 0.155117, err = 0.043050, \u001b[31m   time: 5.196891 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.157881, err = 0.043300\n",
      "\u001b[0m\n",
      "it 14/200, Jtr_pred = 0.148419, err = 0.041550, \u001b[31m   time: 5.439496 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.220927, err = 0.057900\n",
      "\u001b[0m\n",
      "it 15/200, Jtr_pred = 0.149802, err = 0.041933, \u001b[31m   time: 5.200890 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.160137, err = 0.040500\n",
      "\u001b[0m\n",
      "it 16/200, Jtr_pred = 0.151367, err = 0.042500, \u001b[31m   time: 5.421482 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 1/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.159485, err = 0.042700\n",
      "\u001b[0m\n",
      "it 17/200, Jtr_pred = 0.157823, err = 0.042100, \u001b[31m   time: 5.256380 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.191368, err = 0.049500\n",
      "\u001b[0m\n",
      "it 18/200, Jtr_pred = 0.146273, err = 0.041600, \u001b[31m   time: 5.296403 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 2/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.201180, err = 0.053100\n",
      "\u001b[0m\n",
      "it 19/200, Jtr_pred = 0.160838, err = 0.044833, \u001b[31m   time: 5.212903 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.204318, err = 0.051700\n",
      "\u001b[0m\n",
      "it 20/200, Jtr_pred = 0.152382, err = 0.042050, \u001b[31m   time: 5.467456 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 3/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.178415, err = 0.050200\n",
      "\u001b[0m\n",
      "it 21/200, Jtr_pred = 0.145599, err = 0.041250, \u001b[31m   time: 5.319279 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.227842, err = 0.057600\n",
      "\u001b[0m\n",
      "it 22/200, Jtr_pred = 0.154161, err = 0.042150, \u001b[31m   time: 5.314157 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 4/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.162881, err = 0.045500\n",
      "\u001b[0m\n",
      "it 23/200, Jtr_pred = 0.146592, err = 0.040683, \u001b[31m   time: 5.138974 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.207204, err = 0.051400\n",
      "\u001b[0m\n",
      "it 24/200, Jtr_pred = 0.152547, err = 0.042733, \u001b[31m   time: 5.213773 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 5/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.190046, err = 0.049600\n",
      "\u001b[0m\n",
      "it 25/200, Jtr_pred = 0.146646, err = 0.041367, \u001b[31m   time: 5.443621 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.152969, err = 0.040600\n",
      "\u001b[0m\n",
      "it 26/200, Jtr_pred = 0.147085, err = 0.041533, \u001b[31m   time: 5.193520 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 6/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.170042, err = 0.045500\n",
      "\u001b[0m\n",
      "it 27/200, Jtr_pred = 0.142199, err = 0.041600, \u001b[31m   time: 5.173462 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.275525, err = 0.067100\n",
      "\u001b[0m\n",
      "it 28/200, Jtr_pred = 0.147891, err = 0.042000, \u001b[31m   time: 5.177451 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 7/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.198081, err = 0.051700\n",
      "\u001b[0m\n",
      "it 29/200, Jtr_pred = 0.147041, err = 0.041100, \u001b[31m   time: 5.187507 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.159603, err = 0.044100\n",
      "\u001b[0m\n",
      "it 30/200, Jtr_pred = 0.153771, err = 0.041717, \u001b[31m   time: 5.330871 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 8/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.134090, err = 0.035900\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 31/200, Jtr_pred = 0.153177, err = 0.042983, \u001b[31m   time: 5.186730 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.152617, err = 0.041800\n",
      "\u001b[0m\n",
      "it 32/200, Jtr_pred = 0.146831, err = 0.040967, \u001b[31m   time: 5.314614 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 9/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.223229, err = 0.061100\n",
      "\u001b[0m\n",
      "it 33/200, Jtr_pred = 0.152427, err = 0.042133, \u001b[31m   time: 5.261871 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.199176, err = 0.051800\n",
      "\u001b[0m\n",
      "it 34/200, Jtr_pred = 0.143554, err = 0.040483, \u001b[31m   time: 5.179183 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 10/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.203525, err = 0.053900\n",
      "\u001b[0m\n",
      "it 35/200, Jtr_pred = 0.152991, err = 0.043000, \u001b[31m   time: 5.350186 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.157670, err = 0.040200\n",
      "\u001b[0m\n",
      "it 36/200, Jtr_pred = 0.148197, err = 0.041633, \u001b[31m   time: 5.338092 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 11/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.131127, err = 0.037400\n",
      "\u001b[0m\n",
      "it 37/200, Jtr_pred = 0.142891, err = 0.040567, \u001b[31m   time: 5.469422 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.200984, err = 0.053700\n",
      "\u001b[0m\n",
      "it 38/200, Jtr_pred = 0.156378, err = 0.043633, \u001b[31m   time: 3.940431 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 12/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.217737, err = 0.060300\n",
      "\u001b[0m\n",
      "it 39/200, Jtr_pred = 0.144625, err = 0.040567, \u001b[31m   time: 3.510208 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.203476, err = 0.055900\n",
      "\u001b[0m\n",
      "it 40/200, Jtr_pred = 0.147260, err = 0.040950, \u001b[31m   time: 3.552541 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 13/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.199964, err = 0.051500\n",
      "\u001b[0m\n",
      "it 41/200, Jtr_pred = 0.148720, err = 0.042567, \u001b[31m   time: 3.951556 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.168018, err = 0.046900\n",
      "\u001b[0m\n",
      "it 42/200, Jtr_pred = 0.144626, err = 0.041083, \u001b[31m   time: 3.876841 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 14/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.154768, err = 0.042300\n",
      "\u001b[0m\n",
      "it 43/200, Jtr_pred = 0.150136, err = 0.042667, \u001b[31m   time: 3.543175 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.179066, err = 0.049800\n",
      "\u001b[0m\n",
      "it 44/200, Jtr_pred = 0.148968, err = 0.042367, \u001b[31m   time: 4.011840 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 15/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.224010, err = 0.062400\n",
      "\u001b[0m\n",
      "it 45/200, Jtr_pred = 0.152113, err = 0.042783, \u001b[31m   time: 3.820986 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.176764, err = 0.047300\n",
      "\u001b[0m\n",
      "it 46/200, Jtr_pred = 0.153271, err = 0.042783, \u001b[31m   time: 3.820918 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 16/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.173936, err = 0.046100\n",
      "\u001b[0m\n",
      "it 47/200, Jtr_pred = 0.149902, err = 0.042683, \u001b[31m   time: 3.430795 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.157634, err = 0.044100\n",
      "\u001b[0m\n",
      "it 48/200, Jtr_pred = 0.144822, err = 0.041250, \u001b[31m   time: 3.939458 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 17/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.216705, err = 0.059300\n",
      "\u001b[0m\n",
      "it 49/200, Jtr_pred = 0.143170, err = 0.041517, \u001b[31m   time: 3.707925 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.183961, err = 0.048900\n",
      "\u001b[0m\n",
      "it 50/200, Jtr_pred = 0.150875, err = 0.041833, \u001b[31m   time: 3.775491 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 18/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.190735, err = 0.053900\n",
      "\u001b[0m\n",
      "it 51/200, Jtr_pred = 0.146800, err = 0.041933, \u001b[31m   time: 3.953338 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.200916, err = 0.053100\n",
      "\u001b[0m\n",
      "it 52/200, Jtr_pred = 0.144891, err = 0.041400, \u001b[31m   time: 3.788683 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 19/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.149692, err = 0.039900\n",
      "\u001b[0m\n",
      "it 53/200, Jtr_pred = 0.145051, err = 0.042417, \u001b[31m   time: 3.479499 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.191169, err = 0.049500\n",
      "\u001b[0m\n",
      "it 54/200, Jtr_pred = 0.151263, err = 0.042200, \u001b[31m   time: 4.034699 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 20/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.620876, err = 0.131000\n",
      "\u001b[0m\n",
      "it 55/200, Jtr_pred = 0.147435, err = 0.042283, \u001b[31m   time: 3.547628 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 1.063009, err = 0.143400\n",
      "\u001b[0m\n",
      "it 56/200, Jtr_pred = 0.148185, err = 0.042267, \u001b[31m   time: 3.836662 seconds\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m saving weight samples 21/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.193237, err = 0.053000\n",
      "\u001b[0m\n",
      "it 57/200, Jtr_pred = 0.145917, err = 0.041550, \u001b[31m   time: 3.753466 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.157972, err = 0.043000\n",
      "\u001b[0m\n",
      "it 58/200, Jtr_pred = 0.146093, err = 0.041400, \u001b[31m   time: 3.979197 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 22/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169105, err = 0.048400\n",
      "\u001b[0m\n",
      "it 59/200, Jtr_pred = 0.140010, err = 0.039917, \u001b[31m   time: 3.774835 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.162853, err = 0.044700\n",
      "\u001b[0m\n",
      "it 60/200, Jtr_pred = 0.144095, err = 0.041317, \u001b[31m   time: 4.019792 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 23/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.157598, err = 0.041100\n",
      "\u001b[0m\n",
      "it 61/200, Jtr_pred = 0.142017, err = 0.040283, \u001b[31m   time: 3.776772 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.132510, err = 0.039100\n",
      "\u001b[0m\n",
      "it 62/200, Jtr_pred = 0.147215, err = 0.041917, \u001b[31m   time: 3.795991 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 24/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.185147, err = 0.050200\n",
      "\u001b[0m\n",
      "it 63/200, Jtr_pred = 0.144114, err = 0.041250, \u001b[31m   time: 4.004502 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.319323, err = 0.077600\n",
      "\u001b[0m\n",
      "it 64/200, Jtr_pred = 0.159459, err = 0.043417, \u001b[31m   time: 3.702178 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 25/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.142931, err = 0.041400\n",
      "\u001b[0m\n",
      "it 65/200, Jtr_pred = 0.147836, err = 0.041367, \u001b[31m   time: 3.905915 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.181583, err = 0.053000\n",
      "\u001b[0m\n",
      "it 66/200, Jtr_pred = 0.146530, err = 0.041833, \u001b[31m   time: 3.929647 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 26/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.193269, err = 0.048300\n",
      "\u001b[0m\n",
      "it 67/200, Jtr_pred = 0.143301, err = 0.040067, \u001b[31m   time: 3.819596 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.163667, err = 0.042600\n",
      "\u001b[0m\n",
      "it 68/200, Jtr_pred = 0.142088, err = 0.040650, \u001b[31m   time: 3.957477 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 27/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.179364, err = 0.046600\n",
      "\u001b[0m\n",
      "it 69/200, Jtr_pred = 0.147180, err = 0.041733, \u001b[31m   time: 3.865753 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.152836, err = 0.042800\n",
      "\u001b[0m\n",
      "it 70/200, Jtr_pred = 0.147408, err = 0.042067, \u001b[31m   time: 3.920242 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 28/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.201677, err = 0.051600\n",
      "\u001b[0m\n",
      "it 71/200, Jtr_pred = 0.148479, err = 0.041783, \u001b[31m   time: 3.703027 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.162652, err = 0.043100\n",
      "\u001b[0m\n",
      "it 72/200, Jtr_pred = 0.148478, err = 0.041883, \u001b[31m   time: 3.411484 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 29/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.151718, err = 0.044600\n",
      "\u001b[0m\n",
      "it 73/200, Jtr_pred = 0.142937, err = 0.040033, \u001b[31m   time: 3.990622 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.166072, err = 0.045400\n",
      "\u001b[0m\n",
      "it 74/200, Jtr_pred = 0.147647, err = 0.042083, \u001b[31m   time: 3.849164 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 30/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.186564, err = 0.048400\n",
      "\u001b[0m\n",
      "it 75/200, Jtr_pred = 0.146697, err = 0.042450, \u001b[31m   time: 4.030436 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.215906, err = 0.056900\n",
      "\u001b[0m\n",
      "it 76/200, Jtr_pred = 0.146404, err = 0.040950, \u001b[31m   time: 3.427209 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 31/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.220453, err = 0.062100\n",
      "\u001b[0m\n",
      "it 77/200, Jtr_pred = 0.145200, err = 0.041433, \u001b[31m   time: 3.855275 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.141279, err = 0.040300\n",
      "\u001b[0m\n",
      "it 78/200, Jtr_pred = 0.151047, err = 0.042617, \u001b[31m   time: 3.924547 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 32/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.163498, err = 0.045400\n",
      "\u001b[0m\n",
      "it 79/200, Jtr_pred = 0.142410, err = 0.040817, \u001b[31m   time: 3.864713 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.153139, err = 0.043300\n",
      "\u001b[0m\n",
      "it 80/200, Jtr_pred = 0.148792, err = 0.042217, \u001b[31m   time: 3.546687 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 33/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.204127, err = 0.057400\n",
      "\u001b[0m\n",
      "it 81/200, Jtr_pred = 0.141136, err = 0.039250, \u001b[31m   time: 3.464549 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.148649, err = 0.041500\n",
      "\u001b[0m\n",
      "it 82/200, Jtr_pred = 0.146341, err = 0.041517, \u001b[31m   time: 3.586258 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 34/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.151381, err = 0.041500\n",
      "\u001b[0m\n",
      "it 83/200, Jtr_pred = 0.141821, err = 0.040500, \u001b[31m   time: 3.630501 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.147278, err = 0.040700\n",
      "\u001b[0m\n",
      "it 84/200, Jtr_pred = 0.146950, err = 0.041300, \u001b[31m   time: 3.712341 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 35/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.142813, err = 0.042100\n",
      "\u001b[0m\n",
      "it 85/200, Jtr_pred = 0.159414, err = 0.042983, \u001b[31m   time: 3.451904 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.140399, err = 0.037700\n",
      "\u001b[0m\n",
      "it 86/200, Jtr_pred = 0.142626, err = 0.040683, \u001b[31m   time: 3.585561 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 36/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.186889, err = 0.056500\n",
      "\u001b[0m\n",
      "it 87/200, Jtr_pred = 0.142939, err = 0.040583, \u001b[31m   time: 3.665521 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.190345, err = 0.053500\n",
      "\u001b[0m\n",
      "it 88/200, Jtr_pred = 0.147779, err = 0.042100, \u001b[31m   time: 3.537856 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 37/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.160913, err = 0.044000\n",
      "\u001b[0m\n",
      "it 89/200, Jtr_pred = 0.147336, err = 0.041717, \u001b[31m   time: 3.679895 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.151138, err = 0.043400\n",
      "\u001b[0m\n",
      "it 90/200, Jtr_pred = 0.144959, err = 0.041200, \u001b[31m   time: 3.533510 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 38/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.152680, err = 0.039900\n",
      "\u001b[0m\n",
      "it 91/200, Jtr_pred = 0.145253, err = 0.040233, \u001b[31m   time: 3.518805 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.184905, err = 0.051200\n",
      "\u001b[0m\n",
      "it 92/200, Jtr_pred = 0.146545, err = 0.040967, \u001b[31m   time: 3.755753 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 39/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.150077, err = 0.041000\n",
      "\u001b[0m\n",
      "it 93/200, Jtr_pred = 0.147129, err = 0.042233, \u001b[31m   time: 3.530073 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.177953, err = 0.048700\n",
      "\u001b[0m\n",
      "it 94/200, Jtr_pred = 0.141809, err = 0.040617, \u001b[31m   time: 3.666942 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 40/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.142909, err = 0.042000\n",
      "\u001b[0m\n",
      "it 95/200, Jtr_pred = 0.145541, err = 0.041683, \u001b[31m   time: 3.709809 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.209159, err = 0.061600\n",
      "\u001b[0m\n",
      "it 96/200, Jtr_pred = 0.143347, err = 0.041017, \u001b[31m   time: 3.851296 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 41/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.131513, err = 0.036500\n",
      "\u001b[0m\n",
      "it 97/200, Jtr_pred = 0.142752, err = 0.040633, \u001b[31m   time: 3.708214 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.178748, err = 0.049100\n",
      "\u001b[0m\n",
      "it 98/200, Jtr_pred = 0.143802, err = 0.041017, \u001b[31m   time: 3.824051 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 42/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.202243, err = 0.053700\n",
      "\u001b[0m\n",
      "it 99/200, Jtr_pred = 0.141084, err = 0.040633, \u001b[31m   time: 3.636094 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.174419, err = 0.050700\n",
      "\u001b[0m\n",
      "it 100/200, Jtr_pred = 0.137319, err = 0.039867, \u001b[31m   time: 3.788610 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 43/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.167496, err = 0.045800\n",
      "\u001b[0m\n",
      "it 101/200, Jtr_pred = 0.145307, err = 0.041567, \u001b[31m   time: 4.056882 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.172335, err = 0.045300\n",
      "\u001b[0m\n",
      "it 102/200, Jtr_pred = 0.145132, err = 0.042300, \u001b[31m   time: 3.474028 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 44/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.213943, err = 0.054000\n",
      "\u001b[0m\n",
      "it 103/200, Jtr_pred = 0.148365, err = 0.041650, \u001b[31m   time: 3.714070 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.225843, err = 0.061700\n",
      "\u001b[0m\n",
      "it 104/200, Jtr_pred = 0.144750, err = 0.041550, \u001b[31m   time: 3.923718 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 45/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.139662, err = 0.037800\n",
      "\u001b[0m\n",
      "it 105/200, Jtr_pred = 0.146380, err = 0.041717, \u001b[31m   time: 3.704946 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.215833, err = 0.055700\n",
      "\u001b[0m\n",
      "it 106/200, Jtr_pred = 0.142075, err = 0.039933, \u001b[31m   time: 3.640942 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 46/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.178133, err = 0.047100\n",
      "\u001b[0m\n",
      "it 107/200, Jtr_pred = 0.141451, err = 0.040733, \u001b[31m   time: 3.726824 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.159044, err = 0.043700\n",
      "\u001b[0m\n",
      "it 108/200, Jtr_pred = 0.143826, err = 0.040917, \u001b[31m   time: 3.986669 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 47/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.160392, err = 0.043000\n",
      "\u001b[0m\n",
      "it 109/200, Jtr_pred = 0.148293, err = 0.043083, \u001b[31m   time: 3.712094 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.164756, err = 0.046600\n",
      "\u001b[0m\n",
      "it 110/200, Jtr_pred = 0.143123, err = 0.040267, \u001b[31m   time: 3.524743 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 48/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.186498, err = 0.048100\n",
      "\u001b[0m\n",
      "it 111/200, Jtr_pred = 0.140573, err = 0.040017, \u001b[31m   time: 3.553243 seconds\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m    Jdev = 0.309400, err = 0.081700\n",
      "\u001b[0m\n",
      "it 112/200, Jtr_pred = 0.149356, err = 0.042167, \u001b[31m   time: 3.504911 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 49/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.179438, err = 0.047500\n",
      "\u001b[0m\n",
      "it 113/200, Jtr_pred = 0.149804, err = 0.041900, \u001b[31m   time: 3.444447 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.163803, err = 0.040200\n",
      "\u001b[0m\n",
      "it 114/200, Jtr_pred = 0.142719, err = 0.040367, \u001b[31m   time: 3.786177 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 50/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.165068, err = 0.045900\n",
      "\u001b[0m\n",
      "it 115/200, Jtr_pred = 0.144486, err = 0.041183, \u001b[31m   time: 3.632326 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.232177, err = 0.059700\n",
      "\u001b[0m\n",
      "it 116/200, Jtr_pred = 0.149571, err = 0.042550, \u001b[31m   time: 4.084360 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 51/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.176828, err = 0.050500\n",
      "\u001b[0m\n",
      "it 117/200, Jtr_pred = 0.149321, err = 0.042617, \u001b[31m   time: 3.803112 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.136238, err = 0.037800\n",
      "\u001b[0m\n",
      "it 118/200, Jtr_pred = 0.148952, err = 0.041733, \u001b[31m   time: 3.860583 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 52/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.152133, err = 0.041800\n",
      "\u001b[0m\n",
      "it 119/200, Jtr_pred = 0.146315, err = 0.040917, \u001b[31m   time: 3.686179 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.172120, err = 0.048100\n",
      "\u001b[0m\n",
      "it 120/200, Jtr_pred = 0.142431, err = 0.040350, \u001b[31m   time: 3.754016 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 53/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.153652, err = 0.040500\n",
      "\u001b[0m\n",
      "it 121/200, Jtr_pred = 0.143095, err = 0.039917, \u001b[31m   time: 3.652883 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.141681, err = 0.041200\n",
      "\u001b[0m\n",
      "it 122/200, Jtr_pred = 0.146758, err = 0.042550, \u001b[31m   time: 3.625505 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 54/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.149763, err = 0.039800\n",
      "\u001b[0m\n",
      "it 123/200, Jtr_pred = 0.145461, err = 0.041100, \u001b[31m   time: 3.472379 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.255517, err = 0.065200\n",
      "\u001b[0m\n",
      "it 124/200, Jtr_pred = 0.146294, err = 0.041167, \u001b[31m   time: 3.418761 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 55/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.196328, err = 0.052000\n",
      "\u001b[0m\n",
      "it 125/200, Jtr_pred = 0.144741, err = 0.041750, \u001b[31m   time: 3.666344 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.187370, err = 0.046300\n",
      "\u001b[0m\n",
      "it 126/200, Jtr_pred = 0.141536, err = 0.040750, \u001b[31m   time: 3.925810 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 56/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.140205, err = 0.041100\n",
      "\u001b[0m\n",
      "it 127/200, Jtr_pred = 0.139384, err = 0.040167, \u001b[31m   time: 3.910758 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.155075, err = 0.044100\n",
      "\u001b[0m\n",
      "it 128/200, Jtr_pred = 0.144697, err = 0.041500, \u001b[31m   time: 3.664914 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 57/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.155162, err = 0.042000\n",
      "\u001b[0m\n",
      "it 129/200, Jtr_pred = 0.145581, err = 0.041033, \u001b[31m   time: 3.466811 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.241598, err = 0.062100\n",
      "\u001b[0m\n",
      "it 130/200, Jtr_pred = 0.142773, err = 0.041017, \u001b[31m   time: 3.498558 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 58/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.157219, err = 0.045400\n",
      "\u001b[0m\n",
      "it 131/200, Jtr_pred = 0.145213, err = 0.041233, \u001b[31m   time: 4.101603 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.218617, err = 0.056000\n",
      "\u001b[0m\n",
      "it 132/200, Jtr_pred = 0.148855, err = 0.041533, \u001b[31m   time: 3.441540 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 59/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.136290, err = 0.037300\n",
      "\u001b[0m\n",
      "it 133/200, Jtr_pred = 0.145034, err = 0.040850, \u001b[31m   time: 3.897314 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169323, err = 0.047900\n",
      "\u001b[0m\n",
      "it 134/200, Jtr_pred = 0.145506, err = 0.041417, \u001b[31m   time: 3.639822 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 60/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.191620, err = 0.052100\n",
      "\u001b[0m\n",
      "it 135/200, Jtr_pred = 0.144634, err = 0.041717, \u001b[31m   time: 3.473698 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.179174, err = 0.048900\n",
      "\u001b[0m\n",
      "it 136/200, Jtr_pred = 0.142848, err = 0.040883, \u001b[31m   time: 4.228768 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 61/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.150680, err = 0.040300\n",
      "\u001b[0m\n",
      "it 137/200, Jtr_pred = 0.141959, err = 0.039783, \u001b[31m   time: 4.152938 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.205175, err = 0.054000\n",
      "\u001b[0m\n",
      "it 138/200, Jtr_pred = 0.163689, err = 0.044817, \u001b[31m   time: 4.006113 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 62/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.157278, err = 0.044200\n",
      "\u001b[0m\n",
      "it 139/200, Jtr_pred = 0.140421, err = 0.039717, \u001b[31m   time: 4.021819 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.218790, err = 0.059200\n",
      "\u001b[0m\n",
      "it 140/200, Jtr_pred = 0.141765, err = 0.041050, \u001b[31m   time: 3.998469 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 63/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.165837, err = 0.045700\n",
      "\u001b[0m\n",
      "it 141/200, Jtr_pred = 0.144617, err = 0.041283, \u001b[31m   time: 4.075767 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.414218, err = 0.093000\n",
      "\u001b[0m\n",
      "it 142/200, Jtr_pred = 0.150195, err = 0.042183, \u001b[31m   time: 4.051314 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 64/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.188413, err = 0.051200\n",
      "\u001b[0m\n",
      "it 143/200, Jtr_pred = 0.142621, err = 0.040167, \u001b[31m   time: 4.173576 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.153686, err = 0.039500\n",
      "\u001b[0m\n",
      "it 144/200, Jtr_pred = 0.142784, err = 0.039917, \u001b[31m   time: 4.144937 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 65/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.159527, err = 0.047400\n",
      "\u001b[0m\n",
      "it 145/200, Jtr_pred = 0.150892, err = 0.042567, \u001b[31m   time: 4.122610 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.158282, err = 0.046000\n",
      "\u001b[0m\n",
      "it 146/200, Jtr_pred = 0.148433, err = 0.042400, \u001b[31m   time: 4.194870 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 66/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.209284, err = 0.059900\n",
      "\u001b[0m\n",
      "it 147/200, Jtr_pred = 0.149580, err = 0.042200, \u001b[31m   time: 4.387517 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.190715, err = 0.048900\n",
      "\u001b[0m\n",
      "it 148/200, Jtr_pred = 0.145430, err = 0.040883, \u001b[31m   time: 4.187526 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 67/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.132057, err = 0.038700\n",
      "\u001b[0m\n",
      "it 149/200, Jtr_pred = 0.144718, err = 0.040750, \u001b[31m   time: 4.218587 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.170988, err = 0.047800\n",
      "\u001b[0m\n",
      "it 150/200, Jtr_pred = 0.145841, err = 0.040800, \u001b[31m   time: 4.096146 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 68/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.179342, err = 0.046000\n",
      "\u001b[0m\n",
      "it 151/200, Jtr_pred = 0.141373, err = 0.040433, \u001b[31m   time: 4.113335 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.158475, err = 0.044500\n",
      "\u001b[0m\n",
      "it 152/200, Jtr_pred = 0.143311, err = 0.040850, \u001b[31m   time: 4.189720 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 69/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.151315, err = 0.039700\n",
      "\u001b[0m\n",
      "it 153/200, Jtr_pred = 0.140182, err = 0.040250, \u001b[31m   time: 4.028252 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.194979, err = 0.051900\n",
      "\u001b[0m\n",
      "it 154/200, Jtr_pred = 0.147496, err = 0.040767, \u001b[31m   time: 4.075421 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 70/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.159889, err = 0.043200\n",
      "\u001b[0m\n",
      "it 155/200, Jtr_pred = 0.145578, err = 0.041683, \u001b[31m   time: 4.012675 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.168317, err = 0.048400\n",
      "\u001b[0m\n",
      "it 156/200, Jtr_pred = 0.146951, err = 0.041817, \u001b[31m   time: 4.132945 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 71/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.179364, err = 0.049200\n",
      "\u001b[0m\n",
      "it 157/200, Jtr_pred = 0.141688, err = 0.040450, \u001b[31m   time: 4.197250 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.171977, err = 0.046700\n",
      "\u001b[0m\n",
      "it 158/200, Jtr_pred = 0.150155, err = 0.042017, \u001b[31m   time: 4.173043 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 72/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.173148, err = 0.044600\n",
      "\u001b[0m\n",
      "it 159/200, Jtr_pred = 0.153475, err = 0.041233, \u001b[31m   time: 4.274139 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.201653, err = 0.050800\n",
      "\u001b[0m\n",
      "it 160/200, Jtr_pred = 0.143103, err = 0.040483, \u001b[31m   time: 4.282341 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 73/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.149827, err = 0.042500\n",
      "\u001b[0m\n",
      "it 161/200, Jtr_pred = 0.146125, err = 0.041733, \u001b[31m   time: 4.243284 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.155520, err = 0.042100\n",
      "\u001b[0m\n",
      "it 162/200, Jtr_pred = 0.142571, err = 0.041100, \u001b[31m   time: 4.021167 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 74/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.171788, err = 0.049900\n",
      "\u001b[0m\n",
      "it 163/200, Jtr_pred = 0.146703, err = 0.041350, \u001b[31m   time: 3.974698 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.167465, err = 0.045300\n",
      "\u001b[0m\n",
      "it 164/200, Jtr_pred = 0.152743, err = 0.042383, \u001b[31m   time: 4.102562 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 75/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.159503, err = 0.045700\n",
      "\u001b[0m\n",
      "it 165/200, Jtr_pred = 0.148692, err = 0.042167, \u001b[31m   time: 3.985019 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.150519, err = 0.043000\n",
      "\u001b[0m\n",
      "it 166/200, Jtr_pred = 0.139441, err = 0.039783, \u001b[31m   time: 4.010751 seconds\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m saving weight samples 76/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.182312, err = 0.047600\n",
      "\u001b[0m\n",
      "it 167/200, Jtr_pred = 0.142481, err = 0.040433, \u001b[31m   time: 4.095455 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.151144, err = 0.039800\n",
      "\u001b[0m\n",
      "it 168/200, Jtr_pred = 0.138097, err = 0.038967, \u001b[31m   time: 4.109034 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 77/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.309163, err = 0.074800\n",
      "\u001b[0m\n",
      "it 169/200, Jtr_pred = 0.149148, err = 0.041467, \u001b[31m   time: 4.124585 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.186704, err = 0.050500\n",
      "\u001b[0m\n",
      "it 170/200, Jtr_pred = 0.142752, err = 0.041017, \u001b[31m   time: 4.259548 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 78/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.183569, err = 0.051500\n",
      "\u001b[0m\n",
      "it 171/200, Jtr_pred = 0.149818, err = 0.041567, \u001b[31m   time: 4.212407 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.184786, err = 0.050100\n",
      "\u001b[0m\n",
      "it 172/200, Jtr_pred = 0.145404, err = 0.041233, \u001b[31m   time: 4.169191 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 79/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.201328, err = 0.054300\n",
      "\u001b[0m\n",
      "it 173/200, Jtr_pred = 0.144090, err = 0.040633, \u001b[31m   time: 4.172806 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.150055, err = 0.043200\n",
      "\u001b[0m\n",
      "it 174/200, Jtr_pred = 0.145665, err = 0.040600, \u001b[31m   time: 4.071741 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 80/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.306757, err = 0.077200\n",
      "\u001b[0m\n",
      "it 175/200, Jtr_pred = 0.144177, err = 0.040367, \u001b[31m   time: 4.028854 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169944, err = 0.047400\n",
      "\u001b[0m\n",
      "it 176/200, Jtr_pred = 0.148265, err = 0.041967, \u001b[31m   time: 4.079931 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 81/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.151751, err = 0.042500\n",
      "\u001b[0m\n",
      "it 177/200, Jtr_pred = 0.138039, err = 0.039583, \u001b[31m   time: 4.113786 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.145741, err = 0.043100\n",
      "\u001b[0m\n",
      "it 178/200, Jtr_pred = 0.145795, err = 0.041483, \u001b[31m   time: 4.188810 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 82/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.147412, err = 0.037600\n",
      "\u001b[0m\n",
      "it 179/200, Jtr_pred = 0.140671, err = 0.040383, \u001b[31m   time: 4.009089 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.165706, err = 0.049300\n",
      "\u001b[0m\n",
      "it 180/200, Jtr_pred = 0.150856, err = 0.042767, \u001b[31m   time: 4.006131 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 83/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.160214, err = 0.042900\n",
      "\u001b[0m\n",
      "it 181/200, Jtr_pred = 0.143345, err = 0.040617, \u001b[31m   time: 4.118442 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.152030, err = 0.043300\n",
      "\u001b[0m\n",
      "it 182/200, Jtr_pred = 0.144787, err = 0.041517, \u001b[31m   time: 4.253442 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 84/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.181238, err = 0.054500\n",
      "\u001b[0m\n",
      "it 183/200, Jtr_pred = 0.141854, err = 0.040317, \u001b[31m   time: 4.206898 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.237343, err = 0.063100\n",
      "\u001b[0m\n",
      "it 184/200, Jtr_pred = 0.144073, err = 0.040833, \u001b[31m   time: 4.175182 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 85/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.185221, err = 0.049800\n",
      "\u001b[0m\n",
      "it 185/200, Jtr_pred = 0.147327, err = 0.041450, \u001b[31m   time: 4.104582 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.146005, err = 0.039900\n",
      "\u001b[0m\n",
      "it 186/200, Jtr_pred = 0.145080, err = 0.041067, \u001b[31m   time: 4.021947 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 86/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.173730, err = 0.044200\n",
      "\u001b[0m\n",
      "it 187/200, Jtr_pred = 0.146462, err = 0.040967, \u001b[31m   time: 4.060126 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.144732, err = 0.042300\n",
      "\u001b[0m\n",
      "it 188/200, Jtr_pred = 0.139476, err = 0.039517, \u001b[31m   time: 4.190081 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 87/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.177970, err = 0.050700\n",
      "\u001b[0m\n",
      "it 189/200, Jtr_pred = 0.138805, err = 0.039250, \u001b[31m   time: 4.153284 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.171421, err = 0.045400\n",
      "\u001b[0m\n",
      "it 190/200, Jtr_pred = 0.142475, err = 0.039800, \u001b[31m   time: 4.217315 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 88/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.162235, err = 0.043300\n",
      "\u001b[0m\n",
      "it 191/200, Jtr_pred = 0.140622, err = 0.040533, \u001b[31m   time: 4.142345 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.129057, err = 0.039000\n",
      "\u001b[0m\n",
      "it 192/200, Jtr_pred = 0.144924, err = 0.040833, \u001b[31m   time: 4.073982 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 89/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.123110, err = 0.035800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 193/200, Jtr_pred = 0.145538, err = 0.041117, \u001b[31m   time: 4.150653 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.148001, err = 0.040300\n",
      "\u001b[0m\n",
      "it 194/200, Jtr_pred = 0.148033, err = 0.041850, \u001b[31m   time: 4.084058 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 90/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.149176, err = 0.041200\n",
      "\u001b[0m\n",
      "it 195/200, Jtr_pred = 0.140831, err = 0.040133, \u001b[31m   time: 4.240659 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.182438, err = 0.046700\n",
      "\u001b[0m\n",
      "it 196/200, Jtr_pred = 0.143343, err = 0.040767, \u001b[31m   time: 4.204116 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 90/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.166530, err = 0.045900\n",
      "\u001b[0m\n",
      "it 197/200, Jtr_pred = 0.143773, err = 0.040233, \u001b[31m   time: 4.165223 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.150550, err = 0.040400\n",
      "\u001b[0m\n",
      "it 198/200, Jtr_pred = 0.144702, err = 0.040033, \u001b[31m   time: 4.074930 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 90/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.180507, err = 0.048400\n",
      "\u001b[0m\n",
      "it 199/200, Jtr_pred = 0.147830, err = 0.041650, \u001b[31m   time: 3.969674 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.140662, err = 0.039500\n",
      "\u001b[0m\n",
      "\u001b[31m   average time: 4.913495 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m\n",
      "RESULTS:\u001b[0m\n",
      "  cost_dev: 0.123110 (cost_train 0.137319)\n",
      "  err_dev: 0.035800\n",
      "  nb_parameters: 2395210 (2.28MB)\n",
      "  time_per_it: 4.913495s\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "models_dir = 'models_SGLD_MNIST'\n",
    "results_dir = 'results_SGLD_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 128\n",
    "nb_epochs = 200 # We can do less iterations as this method has faster convergence\n",
    "log_interval = 1\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-5\n",
    "prior_sig = 0.1\n",
    "########################################################################################\n",
    "net = Net_langevin(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, N_train=NTrainPointsMNIST, prior_sig=prior_sig)\n",
    "\n",
    "epoch = 0\n",
    "\n",
    "## weight saving parameters #######\n",
    "start_save = 15\n",
    "save_every = 2 # We want less correlated samples -> despite having per minibatch noise we see correlations\n",
    "N_saves = 90\n",
    "###################################\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# train\n",
    "cprint('c', '\\nTrain:')\n",
    "\n",
    "print('  init cost variables:')\n",
    "pred_cost_train = np.zeros(nb_epochs)\n",
    "err_train = np.zeros(nb_epochs)\n",
    "\n",
    "cost_dev = np.zeros(nb_epochs)\n",
    "err_dev = np.zeros(nb_epochs)\n",
    "# best_cost = np.inf\n",
    "best_err = np.inf\n",
    "\n",
    "\n",
    "nb_its_dev = 1\n",
    "\n",
    "tic0 = time.time()\n",
    "for i in range(epoch, nb_epochs):\n",
    "    \n",
    "#     if i in [1]:\n",
    "#         print('updating lr')\n",
    "#         net.sched.step()\n",
    "    \n",
    "    net.set_mode_train(True)\n",
    "\n",
    "    tic = time.time()\n",
    "    nb_samples = 0\n",
    "\n",
    "    for x, y in trainloader:\n",
    "        cost_pred, err = net.fit(x, y)\n",
    "\n",
    "        err_train[i] += err\n",
    "        pred_cost_train[i] += cost_pred\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    pred_cost_train[i] /= nb_samples\n",
    "    err_train[i] /= nb_samples\n",
    "\n",
    "    toc = time.time()\n",
    "    net.epoch = i\n",
    "    # ---- print\n",
    "    print(\"it %d/%d, Jtr_pred = %f, err = %f, \" % (i, nb_epochs, pred_cost_train[i], err_train[i]), end=\"\")\n",
    "    cprint('r', '   time: %f seconds\\n' % (toc - tic))\n",
    "    \n",
    "    # ---- save weights\n",
    "    if i >= start_save and i % save_every == 0:\n",
    "        net.save_sampled_net(max_samples=N_saves)\n",
    "\n",
    "    # ---- dev\n",
    "    if i % nb_its_dev == 0:\n",
    "        net.set_mode_train(False)\n",
    "        nb_samples = 0\n",
    "        for j, (x, y) in enumerate(valloader):\n",
    "\n",
    "            cost, err, probs = net.eval(x, y)\n",
    "\n",
    "            cost_dev[i] += cost\n",
    "            err_dev[i] += err\n",
    "            nb_samples += len(x)\n",
    "\n",
    "        cost_dev[i] /= nb_samples\n",
    "        err_dev[i] /= nb_samples\n",
    "\n",
    "        cprint('g', '    Jdev = %f, err = %f\\n' % (cost_dev[i], err_dev[i]))\n",
    "\n",
    "        if err_dev[i] < best_err:\n",
    "            best_err = err_dev[i]\n",
    "            cprint('b', 'best test error')\n",
    "#             net.save(models_dir+'/theta_best.dat')\n",
    "\n",
    "toc0 = time.time()\n",
    "runtime_per_it = (toc0 - tic0) / float(nb_epochs)\n",
    "cprint('r', '   average time: %f seconds\\n' % runtime_per_it)\n",
    "\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# results\n",
    "cprint('c', '\\nRESULTS:')\n",
    "nb_parameters = net.get_nb_parameters()\n",
    "best_cost_dev = np.min(cost_dev)\n",
    "best_cost_train = np.min(pred_cost_train)\n",
    "err_dev_min = err_dev[::nb_its_dev].min()\n",
    "\n",
    "print('  cost_dev: %f (cost_train %f)' % (best_cost_dev, best_cost_train))\n",
    "print('  err_dev: %f' % (err_dev_min))\n",
    "print('  nb_parameters: %d (%s)' % (nb_parameters, humansize(nb_parameters)))\n",
    "print('  time_per_it: %fs\\n' % (runtime_per_it))\n",
    "\n",
    "\n",
    "\n",
    "## Save results for plots\n",
    "# np.save('results/test_predictions.npy', test_predictions)\n",
    "np.save(results_dir + '/cost_train.npy', pred_cost_train)\n",
    "np.save(results_dir + '/cost_dev.npy', cost_dev)\n",
    "np.save(results_dir + '/err_train.npy', err_train)\n",
    "np.save(results_dir + '/err_dev.npy', err_dev)\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# fig cost vs its\n",
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')\n",
    "\n",
    " \n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### save models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def save_object(obj, filename):\n",
    "#     with open(filename, 'wb') as output:  # Overwrites any existing file.\n",
    "#         pickle.dump(obj, output, pickle.HIGHEST_PROTOCOL)\n",
    "        \n",
    "# save_object(net.weight_set_samples, models_dir+'/state_dicts.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Total params: 2.40M\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "models_dir = 'models_SGLD_MNIST'\n",
    "results_dir = 'results_SGLD_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 128\n",
    "nb_epochs = 200 # We can do less iterations as this method has faster convergence\n",
    "log_interval = 1\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-5\n",
    "prior_sig = 0.1\n",
    "########################################################################################\n",
    "net = Net_langevin(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, N_train=NTrainPointsMNIST, prior_sig=prior_sig)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "with open(models_dir+'/state_dicts.pkl', 'rb') as input:\n",
    "    net.weight_set_samples = pickle.load(input)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inference with sampling on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -828.293518, err = 0.017600\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 200\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "nb_samples = 0\n",
    "test_predictions = np.zeros((10000, 10))\n",
    "\n",
    "Nsamples = 90\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    cost, err, probs = net.sample_eval(x, y, Nsamples, logits=False) # , logits=True\n",
    "\n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "# test_cost /= nb_samples\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.6f\\n' % (-test_cost, test_err))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotations, Bayesian "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First load data into numpy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 1, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "x_dev = []\n",
    "y_dev = []\n",
    "for x, y in valloader:\n",
    "    x_dev.append(x.cpu().numpy())\n",
    "    y_dev.append(y.cpu().numpy())\n",
    "\n",
    "x_dev = np.concatenate(x_dev)\n",
    "y_dev = np.concatenate(y_dev)\n",
    "print(x_dev.shape)\n",
    "print(y_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1, 28, 28)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:83: MatplotlibDeprecationWarning: The set_color_cycle function was deprecated in version 1.5. Use `.set_prop_cycle` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "#############\n",
    "im_ind = 90\n",
    "Nsamples = 90\n",
    "#############\n",
    "\n",
    "angle = 0\n",
    "plt.figure()\n",
    "plt.imshow( ndim.interpolation.rotate(x_dev[im_ind,0,:,:], 0, reshape=False))\n",
    "plt.title('original image')\n",
    "# plt.savefig('original_digit.png')\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 10\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "ims = []\n",
    "predictions = []\n",
    "# percentile_dist_confidence = []\n",
    "x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "\n",
    "fig = plt.figure(figsize=(steps, 8), dpi=80)\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "\n",
    "for i in range(len(rotations)):\n",
    "    \n",
    "    angle = rotations[i]\n",
    "    x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(3, (steps-1), 2*(steps-1)+i)\n",
    "    ax.imshow(x_rot[0,:,:])\n",
    "    ax.axis('off')\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ims.append(x_rot[:,:,:])\n",
    "    \n",
    "ims = np.concatenate(ims)\n",
    "net.set_mode_train(False)\n",
    "y = np.ones(ims.shape[0])*y\n",
    "ims = np.expand_dims(ims, axis=1)\n",
    "cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False) # , logits=True\n",
    "\n",
    "predictions = probs.numpy()\n",
    "    \n",
    "    \n",
    "    \n",
    "textsize = 20\n",
    "lw = 5\n",
    "    \n",
    "print(ims.shape)\n",
    "ims = ims[:,0,:,:]\n",
    "# predictions = np.concatenate(predictions)\n",
    "#print(percentile_dist_confidence)\n",
    "\n",
    "c = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "     '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']  \n",
    "                                         \n",
    "\n",
    "# c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',\n",
    "#      '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
    "\n",
    "ax0 = plt.subplot2grid((3, steps-1), (0, 0), rowspan=2, colspan=steps-1)\n",
    "#ax0 = fig.add_subplot(2, 1, 1)\n",
    "plt.gca().set_color_cycle(c)\n",
    "ax0.plot(rotations, predictions, linewidth=lw)\n",
    "\n",
    "\n",
    "##########################\n",
    "# Dots at max\n",
    "\n",
    "for i in range(predictions.shape[1]):\n",
    "  \n",
    "    selections = (predictions[:,i] == predictions.max(axis=1))\n",
    "    for n in range(len(selections)):\n",
    "        if selections[n]:\n",
    "            ax0.plot(rotations[n], predictions[n, i], 'o', c=c[i], markersize=15.0)\n",
    "##########################  \n",
    "\n",
    "lgd = ax0.legend(['prob 0', 'prob 1', 'prob 2',\n",
    "            'prob 3', 'prob 4', 'prob 5',\n",
    "            'prob 6', 'prob 7', 'prob 8',\n",
    "            'prob 9'], loc='upper right', prop={'size': textsize, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "plt.xlabel('rotation angle')\n",
    "# plt.ylabel('probability')\n",
    "plt.title('True class: %d, Nsamples %d' % (y[0], Nsamples))\n",
    "# ax0.axis('tight')\n",
    "plt.tight_layout()\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "for item in ([ax0.title, ax0.xaxis.label, ax0.yaxis.label] +\n",
    "             ax0.get_xticklabels() + ax0.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "# plt.savefig('percentile_label_probabilities.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "# files.download('percentile_label_probabilities.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0  19  39  59  79  99 119 139 159 179]\n"
     ]
    }
   ],
   "source": [
    "print(rotations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "    \n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "Nsamples = 90\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 16\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "\n",
    "all_preds = np.zeros((len(x_dev), steps, 10))\n",
    "all_sample_preds = np.zeros((len(x_dev), Nsamples, steps, 10))\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "for im_ind in range(len(x_dev)):\n",
    "    x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "    print(im_ind)\n",
    "    \n",
    "    ims = []\n",
    "    predictions = []\n",
    "    for i in range(len(rotations)):\n",
    "\n",
    "        angle = rotations[i]\n",
    "        x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "        ims.append(x_rot[:,:,:])\n",
    "    \n",
    "    ims = np.concatenate(ims)\n",
    "    net.set_mode_train(False)\n",
    "    y = np.ones(ims.shape[0])*y\n",
    "    ims = np.expand_dims(ims, axis=1)\n",
    "    \n",
    "#     cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False)\n",
    "    sample_probs = net.all_sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples)\n",
    "    probs = sample_probs.mean(dim=0)\n",
    "    \n",
    "    all_sample_preds[im_ind, :, :, :] = sample_probs.cpu().numpy()\n",
    "    predictions = probs.cpu().numpy()\n",
    "    all_preds[im_ind, :, :] = predictions\n",
    "   \n",
    "    \n",
    "all_preds_entropy = -(all_preds * np.log(all_preds)).sum(axis=2)\n",
    "mean_angle_entropy = all_preds_entropy.mean(axis=0)\n",
    "std_angle_entropy = all_preds_entropy.std(axis=0)\n",
    "\n",
    "correct_preds = np.zeros((len(x_dev), steps))\n",
    "for i in range(len(x_dev)):\n",
    "    correct_preds[i,:] = all_preds[i,:,y_dev[i]]\n",
    "    \n",
    "correct_mean = correct_preds.mean(axis=0)\n",
    "correct_std = correct_preds.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(results_dir+'/correct_preds.npy', correct_preds)\n",
    "np.save(results_dir+'/all_preds.npy', all_preds)\n",
    "np.save(results_dir+'/all_sample_preds.npy', all_sample_preds) #all_sample_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 90, 16, 10)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):\n",
    "    ax = ax if ax is not None else plt.gca()\n",
    "    if color is None:\n",
    "        color = ax._get_lines.color_cycle.next()\n",
    "    if np.isscalar(yerr) or len(yerr) == len(y):\n",
    "        ymin = y - yerr\n",
    "        ymax = y + yerr\n",
    "    elif len(yerr) == 2:\n",
    "        ymin, ymax = yerr\n",
    "    ax.plot(x, y, color=color)\n",
    "    ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "\n",
    "errorfill(rotations, correct_mean, yerr=correct_std, color=c[2])\n",
    "          \n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel('rotation angle')\n",
    "lgd = plt.legend(['correct class', 'posterior predictive entropy'], loc='upper right',\n",
    "                 prop={'size': 15, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "ax = plt.gca()\n",
    "ax2 = ax.twinx()\n",
    "errorfill(rotations, mean_angle_entropy, yerr=std_angle_entropy, color=c[3], ax=ax2)\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + [ax2.title, ax2.xaxis.label, ax2.yaxis.label] +\n",
    "            ax.get_xticklabels() + ax.get_yticklabels() + ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    item.set_fontsize(15)\n",
    "    item.set_weight('normal')\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight histogram\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23928000,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total parameters: 2392800, samples: 10')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'SGLD'\n",
    "Nsamples = 10\n",
    "# mkdir('weight_samples')\n",
    "weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
    "np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(weight_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(weight_vector, norm_hist=False, label=name, ax=ax)\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('Total parameters: %d, samples: %d' % (len(weight_vector)/Nsamples, Nsamples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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